1. Field of the Invention
The present invention relates generally to an apparatus and method for detecting a subject's motions in captured images, and more particularly, to an apparatus and method for recognizing or detecting a user's hand motions in image frames received from a mobile camera, i.e., a camera embedded in a mobile terminal.
2. Description of the Related Art
Mobile terminals (also known as mobile communication terminals or portable terminals), which were originally developed for voice calls, have evolved into devices that provide many different types of services to users. For example, more recently developed mobile terminals also provide data services such as text messaging, photo and video services, and mobile banking service. Users of mobile terminals with a camera may capture a variety of images with the camera. The mobile terminal with a camera may recognize shapes or patterns in the images, and control an operation of a specific application based on the shape or pattern recognition results.
A conventional hand shape recognition method using a mobile camera predefines recognizable hand shapes or hand patterns, and detects hand shapes in images captured by the camera. More specifically, the mobile terminal searches a database in a memory for a predefined hand shape that best corresponds with the detected hand shape, and triggers an event associated with the search results. Commonly, the conventional hand shape recognition method defines various hand shapes, and diversifies the types of events corresponding to the defined hand shapes.
The conventional hand shape recognition method predefines diverse hand shapes corresponding to input signals, compares a hand shape in the current input image with pre-learned or stored hand shapes, and initiates an event based on the comparison results.
However, for mobile camera-based hand shape recognition applications, it is important to generate hand shape-triggered events that are robust to changes in the use environment. In the conventional hand shape recognition method, a database for hand shape learning and recognition has been implemented under the assumption that in any environment, the background color is not similar to the skin color and part of user's body, like the face, are not covered. Consequently, disturbing factors such as a change in lighting or scale, rotational displacement, background color, or covering, may significantly degrade recognition performance. Additionally, a task such as hand shape recognition demands significant computing resources that are necessarily limited in the mobile environment.